Genomics euphoria: ramblings of a scientist on genetics, genomics and the meaning of life

Category Archives: Cancer research

Letters from the trenches of war on cancer (Part I)

As I get older, cancer surpasses a scientific curiosity and morphs itself into a harsher reality. As our parents start to get worried about every mole and lump, we also accompany them through the ensuing emotional roller coaster. Working close to a hospital is not helping either… while the tumor samples you see every day are assigned random numbers, it is quite impossible not to see the human suffering behind every biopsy. While I still firmly and deeply believe in the fact that ultimately it is the basic research that can revolutionize health and medicine, I can also sense the urgency of now and the need to act on that front. It is this dichotomy that has shaped my research for the past few years, the fruits of which are finding their way into the annals of science.

It is not news to anyone that I study the biology and regulation of RNA (see the two previous posts on this very blog: here and here). I have specifically focused on developing computational and experimental frameworks that help reveal the identity of post-transcriptional regulatory programs and their underlying molecular mechanisms. Towards the end of my tenure as a graduate student, building upon the work by talented postdocs in the Tavaozie lab at Princeton University (namely Olivier  and Noam who published their work back in 2008) and with the help of my genius friend Hamed, we developed, benchmarked and validated a computational method named TEISER that extends motif-finding algorithms into the world of RNA by taking into account the local secondary structure of RNA molecules as well as their sequence.

When I started out as a postdoc, my goal was to study post-transcriptional regulation using cancer metastasis as a model. In addition to its clinical impact, studying metastasis also has the added benefit of access to a large compendium of high-quality datasets as well as rigorous in vivo and in vitro models for downstream validation of interesting findings.

When it comes to tumorigenesis in general, there is a large body of work focusing on the role of transcriptional regulation, specifically  transcription factors as suppressors and promoters of oncogenesis. However, other aspects of RNA life-cycle are substantially understudied. The success of our lab and many others in revealing novel and uncharacterized regulatory networks based on the action of various miRNA in driving or suppressing metastasis highlights the possibility that heretofore uncharacterized post-transcriptional regulatory programs may play instrumental roles in tumorigenesis.

Given the success of miRNA regulation and my previous work on RNA stability, performing differential transcript stability measurements between highly metastatic cells relative to their poorly metastatic parental populations seemed like a logical step. Using thiouridin pulse-chase labeling and capture followed by high-throughput RNA-seq, we estimated decay rates for every detectable transcript (~13000 transcripts total). It was around this dataset that we built an ambitious study, pushing ourselves to dig deeper at every step. We generated, analyzed, and interpreted heaps of data of various kinds: in silico, in vitro, and in vivo. The results of this study was the discovery of a novel post-transcriptional regulatory program that promotes breast cancer metastasis. Our results were recently published in Nature, however, I also gained insights that could not be included in a 4-page paper. As such, in the upcoming posts, I’ll try and expand on various aspects of this study that I found fascinating. Stay tuned…

War on cancer: Notes from the frontlines

Last month, I was fortunate enough to attend the tumor heterogeneity and plasticity symposium. The conference which was jointly organized by CNIO and Nature, was held in Madrid. I thought it would be a good idea for me to write some notes on what I heard at the conference.


  1. There were two keynote speakers, Kornelia Polyak from Dana Farber and José Baselga from across the street from us at MSKCC. Jose’s talk was very much clinical as he enumerated the MANY trials that they are conducting. Kornelia’s talk, on the other hand, was more basic-research-y (is that a word? if not, it should be…). I really cannot distill down a whole keynote into a few sentences, but the bottom-line was: (i) diversity is bad; (ii) we can develop rational experimental approaches to find cancer drivers that are sub-clonal; (iii) sub-clonality brings forth the idea of growth-promoters vs. competitors. All in all, a very good and complex talk… Maybe one of the few talks at the conference with significant mechanistic and functional observations.
  2. The field, given how young it is, is very descriptive. It largely involves researchers making cool observations… don’t get me wrong, I don’t mean it in a derogatory sense; what I am trying to say is that it would probably take years before we can make sense of many of the observations.
  3. On the sequencing side, Elaine Mardis talked about very deep whole-genome sequencing of matched primary and metastatic tumors, followed by rigorous validations of each genetic variation. She talked about tumor lineage and pressures exerted by therapeutic manipulations and how they shape the heterogeneity of the metastatic sites. One interesting thing that she mentioned was that very few single-nucleotide variations are actually expressed (I think she said something like 44%).
  4. Sean Morrison gave a very rigorous presentation on heterogeneity and metastasis. He had used genomic tools plus xenografting in NSG mice to study melanoma.
  5. Dana Pe’er talked about analyzing mass-cytometry (Cy-TOF) data using ViSNE. Cy-TOF follows the same logic as FACS, with the main difference that instead of fluorophores, other elements with distinct and sharp mass-spec peaks are conjugated to antibodies. About 40 markers can be measured simultaneously for each cell (compared to 7-8 for FACS). However, making sense of a 40-dimensional dataset is not straightforward, which is why ViSNE comes into play. This tool however, can be used for other types of dimensionality reduction approaches as well. Think of it as non-linear PCA…
  6. Charles Swanton spoke about spatial heterogeneity in tumors where multiple biopsies from the same tumor were sequenced. The level of heterogeneity was scary… For example, we find driver mutations based on their clonality and we aim to target them therapeutically, but there were sub-populations in the tumor that had already lost the driver mutations even in the absence of therapeutic selection pressure.
  7. A number of speakers touched on this idea that the resistant population is already present in the primary tumor and does not necessarily arise after treatment.

All in all, I met some new people and listened to some amazing talks…